CN111598162A - Cattle risk monitoring method, terminal equipment and storage medium - Google Patents
Cattle risk monitoring method, terminal equipment and storage medium Download PDFInfo
- Publication number
- CN111598162A CN111598162A CN202010406854.5A CN202010406854A CN111598162A CN 111598162 A CN111598162 A CN 111598162A CN 202010406854 A CN202010406854 A CN 202010406854A CN 111598162 A CN111598162 A CN 111598162A
- Authority
- CN
- China
- Prior art keywords
- cattle
- registration
- registration request
- gaussian distribution
- distribution model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Business, Economics & Management (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention provides a cattle risk monitoring method, terminal equipment and a storage medium, wherein the method comprises the following steps: acquiring a registration request and calling registration record information according to the registration request; and inputting the characteristic information corresponding to the registration record information into a pre-trained Gaussian distribution model to obtain a cattle recognition result. The method can more accurately find out the hidden cattle, reduce the problem of medical resource registration shortage caused by cattle registration and ensure that the distribution of medical resources is fairer and more reasonable.
Description
Technical Field
The invention relates to the technical field of medical registration, in particular to a cattle risk monitoring method, terminal equipment and a storage medium.
Background
In the field of medical registration, due to a more complex registration channel and extremely unbalanced medical requirements, supply and demand of partial large hospitals and expert number sources are short, and conditions are created for the active existence of scalper parties. Particularly, more and more number sources are put on line from off-line channels, so that more convenient registration service is provided for users, and more suitable conditions are provided for the spreading of cattle. These online cattle are often bought in large numbers by breaking software, registering in batches, waiting for a means to collect the number sources, and then selling them to users who are urgently needed to seek medical advice to earn a riot from them.
At present, whether the registered person is the registered person can not be accurately identified in the prior art, so that the cattle is organically superior, and the technical problem of harming public interests is caused.
Disclosure of Invention
The invention aims to provide a cattle risk monitoring method, terminal equipment and a storage medium, which can be used for more accurately finding out hidden cattle and reducing the problem of medical resource registration shortage caused by cattle registration, so that the distribution of medical resources is fairer and more reasonable.
The technical scheme provided by the invention is as follows:
the invention provides a cattle risk monitoring method, which comprises the following steps:
acquiring a registration request and calling registration record information according to the registration request;
and inputting the characteristic information corresponding to the registration record information into a pre-trained Gaussian distribution model to obtain a cattle recognition result.
Further, the steps of obtaining the registration request and calling the registration record information according to the registration request include:
acquiring a training set and a test set; the training set and the test set respectively contain cattle registration sample data and non-cattle registration sample data;
acquiring a feature set corresponding to each sample data in the training set;
establishing a Gaussian distribution model according to the feature set, inputting feature information corresponding to registration data in the test set into the Gaussian distribution model, and calculating to obtain the identification accuracy of the Gaussian distribution model;
and determining whether to adjust the parameter variable of the Gaussian distribution model according to the accuracy, and determining the Gaussian distribution model with the accuracy reaching a preset value as the pre-trained Gaussian distribution model.
Further, the acquiring the registration request and calling the registration record information according to the registration request includes the steps of:
acquiring the registration request from a target registration channel;
and inquiring and acquiring registration record information according to the user identity information in the registration request.
Further, the step of inputting the feature information corresponding to the registration record information into a pre-trained gaussian distribution model to obtain a cattle identification result includes:
inputting the characteristic information corresponding to the registration record information into the Gaussian distribution model to output a probability numerical value;
if the probability value reaches a first preset threshold value, determining that the user identity corresponding to the registration request is a non-cattle;
if the probability numerical value reaches a second preset threshold value and does not reach a first preset threshold value, determining that the user identity corresponding to the registration request is a suspected cattle;
and if the probability numerical value does not reach a second preset threshold value, determining that the user identity corresponding to the registration request is a cattle.
Further, the step of inputting the feature information corresponding to the registration record information into a pre-trained gaussian distribution model to obtain a cattle recognition result includes:
if the user identity is not a cattle, receiving the registration request;
if the user identity is not a cattle, initiating identity authentication and selecting whether to accept the registration request or not according to an authentication result;
and if the user identity is the cattle, rejecting the registration request.
The present invention also provides a terminal device, including:
the data acquisition module is used for acquiring a registration request and calling registration record information according to the registration request;
and the cattle recognition module is used for inputting the characteristic information corresponding to the registration record information into a pre-trained Gaussian distribution model to obtain a cattle recognition result.
Further, the method also comprises the following steps:
the sample acquisition module is used for acquiring a training set and a test set; the training set and the test set respectively contain cattle registration sample data and non-cattle registration sample data;
the characteristic extraction module is used for acquiring a characteristic set corresponding to each sample data in the training set;
and the model acquisition module is used for establishing a Gaussian distribution model according to the feature set, inputting feature information corresponding to the registration data in the test set into the Gaussian distribution model, calculating the identification accuracy of the Gaussian distribution model, determining whether to adjust the parameter variable of the Gaussian distribution model according to the accuracy, and determining the Gaussian distribution model with the accuracy reaching a preset value as the pre-trained Gaussian distribution model.
Further, the cattle identification module comprises:
the processing unit is used for inputting the characteristic information corresponding to the registration record information into the Gaussian distribution model and outputting a probability numerical value;
the identification unit is used for determining that the user identity corresponding to the registration request is a non-cattle if the probability value reaches a first preset threshold value; if the probability numerical value reaches a second preset threshold value and does not reach a first preset threshold value, determining that the user identity corresponding to the registration request is a suspected cattle; and if the probability numerical value does not reach a second preset threshold value, determining that the user identity corresponding to the registration request is a cattle.
Further, the method also comprises the following steps:
the processing module is used for receiving the registration request if the user identity is not a cattle; if the user identity is not a cattle, initiating identity authentication and selecting whether to accept the registration request or not according to an authentication result; and if the user identity is the cattle, rejecting the registration request.
The invention also provides a storage medium, which stores at least one instruction, and the instruction is loaded and executed by a processor to implement the operation performed by the cattle risk monitoring method.
By the method, the terminal equipment and the storage medium for monitoring the cattle risk, hidden cattle can be found out more accurately, the problem of medical resource registration shortage caused by cattle registration is solved, and medical resource distribution is fairer and more reasonable.
Drawings
The above features, technical features, advantages and implementations of a cattle risk monitoring method, a terminal device and a storage medium will be further explained in a clearly understandable manner with reference to the accompanying drawings.
FIG. 1 is a flow chart of one embodiment of a method of risk monitoring of cattle of the present invention;
FIG. 2 is a flow chart of another embodiment of a method of risk monitoring of cattle of the present invention;
FIG. 3 is a flow chart of another embodiment of a method of risk monitoring of cattle of the present invention;
fig. 4 is a schematic structural diagram of an embodiment of a terminal device of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "one" means not only "only one" but also a case of "more than one".
One embodiment of the present invention, as shown in fig. 1, is a method for monitoring cattle risk, comprising:
s100, acquiring a registration request and calling registration record information according to the registration request;
specifically, the registration request includes, but is not limited to, registration time information, registration hospital information, registration department information, registration user identity information (including, but not limited to, user name, member number, patient number, mobile phone number, and identification number), and registration network address (IP address). And after the terminal equipment acquires the registration request, calling corresponding registration record information according to the registration request.
S200, inputting the characteristic information corresponding to the registration record information into a pre-trained Gaussian distribution model to obtain a cattle recognition result.
Specifically, after the terminal device obtains the registration record information, the registration record information is input into a pre-trained Gaussian distribution model, and the Gaussian distribution model is used for assisting in identifying whether a user to which a registration request corresponding to the registration record information belongs is a cattle, so that the terminal device obtains a cattle identification result.
In the embodiment, registration record information is obtained according to a registration request of a user and then is input into the Gaussian distribution model for classification and identification, so that hidden cattle users can be found out more accurately, the problem of medical resource registration shortage caused by registration of the cattle users is reduced, and medical resources are distributed more fairly.
One embodiment of the present invention, as shown in fig. 2, is a method for monitoring cattle risk, comprising:
s010 obtains a training set and a test set; the training set and the test set respectively contain cattle registration sample data and non-cattle registration sample data;
specifically, the terminal device obtains a large amount of registration data from each hospital, and the registration data have labels, so that the registration data are classified according to the labels to obtain cattle sample data and non-cattle sample data. And screening a first preset number of cattle sample data, taking a second preset number of non-cattle sample data as a training set, screening a third preset number of cattle sample data, and taking a fourth preset number of non-cattle sample data as a test set. The first preset number is larger than the third preset number, and the second preset number is larger than the fourth preset number.
S020 obtaining a feature set corresponding to each sample data in the training set;
specifically, feature extraction is performed on each cattle registration sample data and non-cattle sample data in the training set to obtain all feature information corresponding to each sample data in the training set. The feature types corresponding to the feature information include, but are not limited to, user identity information (including a membership number and a user name), the number of times of canceling appointment registration, the total number of times of registering appointment registration, the occupation ratio of canceling appointments, the number of times of registering within a first preset time (e.g. 30 seconds), the class of registered departments and the number of times of registering corresponding to the class of the departments, the number of times of giving a discount, and the number of times of registering within a second preset time (e.g. 30 minutes). The characteristic types can represent the behavior characteristics of the user, and the terminal equipment establishes a characteristic set corresponding to each sample data according to all characteristic information corresponding to each sample data.
Preferably, the feature set corresponding to each sample data further includes an association relationship between feature information. For example, there is a direct proportional relationship between the number of times of canceling reservation registration and the ratio of canceling reservation registration, that is, the ratio of canceling reservation registration increases with the increase of the number of times of canceling reservation registration, and there is an inverse proportional relationship between the total number of times of reserving registration and the ratio of canceling reservation registration, that is, the ratio of canceling reservation registration decreases with the increase of the total number of times of reserving registration.
S030 establishes a Gaussian distribution model according to the feature set, inputs feature information corresponding to registration data in the test set into the Gaussian distribution model, and calculates to obtain the identification accuracy of the Gaussian distribution model;
s040 determines whether to adjust the parameter variable of the Gaussian distribution model according to the accuracy, and determines the Gaussian distribution model with the accuracy reaching a preset value as a pre-trained Gaussian distribution model;
s100, acquiring a registration request and calling registration record information according to the registration request;
s200, inputting the characteristic information corresponding to the registration record information into a pre-trained Gaussian distribution model to obtain a cattle recognition result.
Specifically, it is assumed that feature information in a feature set corresponding to each sample data conforms to gaussian distribution, and if feature information that does not conform to gaussian distribution exists, the feature information is processed, so that the feature information that does not conform to gaussian distribution conforms to gaussian distribution after processing. For example, if a certain feature information x does not conform to gaussian distribution, the feature information x is subjected to data operation to obtain processed feature information log (x). Other processing modes that the feature information not conforming to the gaussian distribution conforms to the gaussian distribution after being processed belong to the protection scope of the present invention, and are not described in detail herein.
After a multivariate Gaussian distribution model is established according to the feature set, data distribution is observed through cattle data and non-cattle data in the training set, so that a first preset threshold value and a second preset threshold value are obtained, wherein the first preset threshold value is smaller than the second preset threshold value.
And performing feature extraction on each cattle registration sample data and non-cattle sample data in the test set to obtain all feature information corresponding to each sample data in the test set, and inputting the feature information corresponding to the current registration sample data in the test set into a Gaussian distribution model to output a corresponding probability numerical value. And if the probability numerical value is higher than a first preset threshold value, the current registration sample data is the registration data of the non-cattle user, and if the probability numerical value is lower than a second preset threshold value, the current registration sample data is the registration data of the non-cattle user. And comparing the judgment result with the label type (cattle or non-cattle) of the current registration sample data in the test set, thereby obtaining the cattle identification result of the current registration sample data. And by analogy, switching the next registration sample data in the test set, repeating the steps until the identification results of all the registration sample data in the test set are obtained, and counting according to the identification results to obtain the identification accuracy of the Gaussian distribution model.
And if the identification accuracy rate does not reach the preset value, determining to adjust the parameter variable of the Gaussian distribution model until the identification accuracy rate of the adjusted Gaussian distribution model after repeated testing according to the steps reaches the preset value. And if the identification accuracy reaches a preset value, determining not to adjust the parameter variable of the Gaussian distribution model.
In the embodiment, a Gaussian distribution model is established through a training set and a test set, various threshold values of the identified cattle are determined, a data judgment basis is provided for cattle discrimination, hidden cattle users can be effectively, efficiently and accurately found out, the problem of medical resource registration shortage caused by registration of the cattle users is reduced, medical resources are distributed more fairly, cattle selling behaviors can be stricken strictly, the behaviors that the cattle occupy the registration resources are effectively stopped, and economic loss caused by the cattle is reduced.
One embodiment of the present invention, as shown in fig. 3, is a method for monitoring cattle risk, comprising:
s110, acquiring a registration request from a target registration channel;
s120, registration record information is obtained according to the user identity information inquiry in the registration request;
specifically, the target registration channel includes, but is not limited to, a hospital service window, a registration website such as a hospital self-help registration machine and a micro medical network, and a server network address cooperating with a trusted third-party medical service institution. And the terminal equipment acquires the registration request from the target registration channel at the current moment.
The registration request includes, but is not limited to, registration time information, registration hospital information, registration department information, registration user identity information, registration network address (IP address). After the terminal device acquires the registration request, registration record information is called from a local server of the hospital (a hospital service window, data of a self-help registration machine of the hospital can be synchronized to the local server of the hospital) according to the registration request, and of course, if each large hospital cooperates with a credible third-party medical service institution, all registration record information of a user corresponding to the registration request can be called from the server of the third-party medical service institution.
S210, inputting the characteristic information corresponding to the registration record information into a Gaussian distribution model to output a probability numerical value;
s220, if the probability numerical value reaches a first preset threshold value, determining that the user identity corresponding to the registration request is a non-cattle;
s230, if the probability numerical value reaches a second preset threshold value and does not reach the first preset threshold value, determining that the user identity corresponding to the registration request is a suspected cattle;
s240, if the probability numerical value does not reach a second preset threshold value, determining that the user identity corresponding to the registration request is a cattle;
specifically, after the terminal device acquires the registration record information, the terminal device extracts the feature information of the registration record information, and then inputs the feature information of the registration record information into a Gaussian distribution model, and the Gaussian distribution model identifies and outputs a corresponding probability numerical value for the feature information of the registration record information. And if the probability value reaches a first preset threshold value, determining that the user identity corresponding to the registration request is a non-cattle. And if the probability value reaches the second preset threshold value and does not reach the first preset threshold value, determining that the user identity corresponding to the registration request is suspected cattle, and at the moment, further verifying the user identity corresponding to the registration request by the terminal equipment. And if the probability numerical value does not reach a second preset threshold value, determining that the user identity corresponding to the registration request is a cattle. And the terminal equipment identifies whether the user initiating the registration request is the cattle or not according to the characteristic information corresponding to the registration record information, so that the terminal equipment obtains the cattle identification result.
S250, if the user identity is not a cattle, receiving a registration request;
s260, if the user identity is not a cattle, initiating identity verification and selecting whether to accept a registration request according to a verification result;
s270, if the user identity is the cattle, rejecting the registration request.
Specifically, if the user identity is a non-cattle, the registration request is accepted, and if the user identity is a cattle, the registration request is rejected. If the user identity is suspected cattle, sending a verification problem to carry out identity verification, and if the verification is passed, accepting the registration request, or else, rejecting the registration request.
In the embodiment, as the number of medical seekers is large and the number of medical resources is small, a large number of yellow cattle can be introduced to collect the number sources and then sell the number sources at a high price, the appearance of the yellow cattle greatly damages the benefits of users, so that patients who really have medical needs can not hang the number, the experience of online appointment registration is greatly reduced, and the benefits of the public are harmed. The method has the advantages that the Gaussian distribution model is established through the training set and the testing set, various threshold values of the identified cattle are determined, data judgment basis is provided for cattle discrimination, hidden cattle users can be effectively, efficiently and accurately found out, the problem of medical resource registration shortage caused by registration of the cattle users is reduced, medical resources are distributed more fairly, cattle selling behaviors can be stricken strictly, the behaviors that the cattle occupy registration resources are effectively prevented, and economic loss caused by the cattle is reduced. According to the behavior characteristics of the user, a real-time basis can be provided for real-time cattle interception. In addition, the registration request determined to be the cattle is rejected, so that the allocation of medical registration resources is more reasonable and fair.
The user identity of the registration request is screened through the embodiment, so that concentrated registration of the cattle can be avoided, limited medical resources are utilized fairly and efficiently, the problem that the cattle cannot be used for medical treatment easily due to the fact that the cattle is sold for registration is effectively solved, and the fairness and the reasonability of medical resource distribution are improved.
Preferably, if the user identity is suspected cattle, a verification problem is sent to the user for identity verification, and whether to accept the registration request is selected according to a verification result. And if the treatment record of the user cannot be inquired, acquiring the relative identity information input by the user, and generating a verification problem and a corresponding problem answer according to the latest treatment record corresponding to the input relative identity information. And if the treatment record of the user can be inquired, generating a verification question and a corresponding question answer according to the latest treatment record corresponding to the identity information of the user. And if the input answer is the same as the answer of the question, determining to accept the registration request, and if the input answer is not the same as the answer of the question, determining to reject the registration request. The identity of the user who initiates the registration request can be identified through verification, so that limited medical resources are utilized fairly and efficiently, and the problem of difficulty in hospitalizing caused by the resale registration of the cattle is solved effectively.
Preferably, if the user identity is suspected cattle, a face recognition verification request is initiated, the user is prompted to use the mobile terminal initiating the registration request to perform image acquisition so as to obtain a face image video of the user, and according to the face image video and the identity card head image in the registration request, a face recognition algorithm is adopted to perform comparison so as to obtain the similarity of face recognition. And if the similarity reaches the preset similarity value, determining to accept the registration request, and if the similarity does not reach the preset similarity value, determining to reject the registration request. The identity of the user who initiates the registration request is discriminated through face recognition, so that limited medical resources are utilized fairly and efficiently, and the problem of difficulty in hospitalizing caused by the fact that the cattle is sold for registration is effectively solved. Furthermore, the method is simple. Face recognition is introduced in the registration reservation process, identity authentication of a user is enhanced, the behavior that the cattle occupies registration resources is effectively prevented, and economic loss caused by the cattle is reduced. Moreover, the method can limit the registration of the cattle by the owner, and avoid the market disturbance of the cattle. The problem of difficult seeing a doctor that leads to because cattle at present is solved, make the distribution of medical resource more equitable, use more high-efficient.
In an embodiment of the present invention, as shown in fig. 4, a terminal device includes:
the data acquisition module 10 is used for acquiring a registration request and calling registration record information according to the registration request;
and the cattle recognition module 20 is configured to input the feature information corresponding to the registration record information into a pre-trained gaussian distribution model to obtain a cattle recognition result.
Specifically, this embodiment is a device embodiment corresponding to the method embodiment, and specific effects refer to the method embodiment, which is not described in detail herein.
Based on the foregoing embodiment, further comprising:
the sample acquisition module is used for acquiring a training set and a test set; the training set and the test set respectively contain cattle registration sample data and non-cattle registration sample data;
the characteristic extraction module is used for acquiring a characteristic set corresponding to each sample data in the training set;
the model acquisition module is used for establishing a Gaussian distribution model according to the feature set, inputting feature information corresponding to the registration data in the test set into the Gaussian distribution model, calculating the identification accuracy of the Gaussian distribution model, determining whether to adjust parameter variables of the Gaussian distribution model according to the accuracy, and determining the Gaussian distribution model with the accuracy reaching a preset value as a pre-trained Gaussian distribution model.
Specifically, this embodiment is a device embodiment corresponding to the method embodiment, and specific effects refer to the method embodiment, which is not described in detail herein.
Based on the foregoing embodiment, the cattle identification module 20 includes:
the processing unit is used for inputting the characteristic information corresponding to the registration record information into the Gaussian distribution model and outputting a probability numerical value;
the identification unit is used for determining that the user identity corresponding to the registration request is a non-cattle if the probability value reaches a first preset threshold value; if the probability numerical value reaches a second preset threshold value and does not reach the first preset threshold value, determining that the user identity corresponding to the registration request is a suspected cattle; and if the probability numerical value does not reach a second preset threshold value, determining that the user identity corresponding to the registration request is a cattle.
Specifically, this embodiment is a device embodiment corresponding to the method embodiment, and specific effects refer to the method embodiment, which is not described in detail herein.
Based on the foregoing embodiment, further comprising:
the processing module is used for receiving a registration request if the user identity is not a cattle; if the user identity is not a cattle, initiating identity authentication and selecting whether to accept a registration request according to an authentication result; and if the user identity is the cattle, rejecting the registration request.
Specifically, this embodiment is a device embodiment corresponding to the method embodiment, and specific effects refer to the method embodiment, which is not described in detail herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of program modules is illustrated, and in practical applications, the above-described distribution of functions may be performed by different program modules, that is, the internal structure of the apparatus may be divided into different program units or modules to perform all or part of the above-described functions. Each program module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one processing unit, and the integrated unit may be implemented in a form of hardware, or may be implemented in a form of software program unit. In addition, the specific names of the program modules are only used for distinguishing the program modules from one another, and are not used for limiting the protection scope of the application.
The terminal equipment can be desktop computers, notebooks, palm computers, tablet computers, mobile phones, man-machine interaction screens and other equipment. It will be appreciated by those skilled in the art that the foregoing is merely an example of a terminal device and is not limiting, and that more or fewer components than the foregoing embodiments may be included, or certain components may be combined, or different components may be included, such as: the terminal device may also include input/output interfaces, display devices, network access devices, communication buses, communication interfaces, and the like. A communication interface and a communication bus, and may further comprise an input/output interface, wherein the processor, the memory, the input/output interface and the communication interface complete communication with each other through the communication bus. The memory stores a computer program, and the processor is used for executing the computer program stored on the memory to realize the cattle risk monitoring method in the method embodiment.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be an internal storage unit of the terminal device, such as: hard disk or memory of the terminal device. The memory may also be an external storage device of the terminal device, such as: the terminal equipment is provided with a plug-in hard disk, an intelligent memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like. Further, the memory may also include both an internal storage unit and an external storage device of the terminal device. The memory is used for storing the computer program and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.
A communication bus is a circuit that connects the described elements and enables transmission between the elements. For example, the processor receives commands from other elements through the communication bus, decrypts the received commands, and performs calculations or data processing according to the decrypted commands. The memory may include program modules such as a kernel (kernel), middleware (middleware), an Application Programming Interface (API), and applications. The program modules may be comprised of software, firmware or hardware, or at least two of the same. The input/output interface forwards commands or data entered by a user via the input/output interface (e.g., sensor, keyboard, touch screen). The communication interface connects the terminal equipment with other network equipment, user equipment and a network. For example, the communication interface may be connected to a network by wire or wirelessly to connect to external other network devices or user devices. The wireless communication may include at least one of: wireless fidelity (WiFi), Bluetooth (BT), Near Field Communication (NFC), Global Positioning Satellite (GPS) and cellular communications, among others. The wired communication may include at least one of: universal Serial Bus (USB), high-definition multimedia interface (HDMI), asynchronous transfer standard interface (RS-232), and the like. The network may be a telecommunications network and a communications network. The communication network may be a computer network, the internet of things, a telephone network. The terminal device may be connected to the network via a communication interface, and a protocol used by the terminal device to communicate with other network devices may be supported by at least one of an application, an Application Programming Interface (API), middleware, a kernel, and a communication interface.
In an embodiment of the present invention, a storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the operations performed by the embodiments of the cattle risk monitoring method. For example, the computer readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device and media embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference may be made to some descriptions of the method embodiments for relevant points.
The device and the medium provided by the embodiment of the invention correspond to the method one by one, so the device and the medium also have the beneficial technical effects similar to the corresponding method.
They may be implemented in program code that is executable by a computing device such that it is executed by the computing device, or separately, or as individual integrated circuit modules, or as a plurality or steps of individual integrated circuit modules. Thus, the present invention is not limited to any specific combination of hardware and software.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or recited in detail in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by sending instructions to relevant hardware through a computer program, where the computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises: computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the content of the computer-readable storage medium can be increased or decreased according to the requirements of the legislation and patent practice in the jurisdiction, for example: in certain jurisdictions, in accordance with legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunications signals.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A cattle risk monitoring method is characterized by comprising the following steps:
acquiring a registration request and calling registration record information according to the registration request;
and inputting the characteristic information corresponding to the registration record information into a pre-trained Gaussian distribution model to obtain a cattle recognition result.
2. The method for cattle risk monitoring according to claim 1, wherein the step of obtaining a registration request and retrieving registration record information according to the registration request comprises:
acquiring a training set and a test set; the training set and the test set respectively contain cattle registration sample data and non-cattle registration sample data;
acquiring a feature set corresponding to each sample data in the training set;
establishing a Gaussian distribution model according to the feature set, inputting feature information corresponding to registration data in the test set into the Gaussian distribution model, and calculating to obtain the identification accuracy of the Gaussian distribution model;
and determining whether to adjust the parameter variable of the Gaussian distribution model according to the accuracy, and determining the Gaussian distribution model with the accuracy reaching a preset value as the pre-trained Gaussian distribution model.
3. The method for cattle risk monitoring according to claim 1, wherein the step of obtaining a registration request and retrieving registration record information according to the registration request comprises the steps of:
acquiring the registration request from a target registration channel;
and inquiring and acquiring registration record information according to the user identity information in the registration request.
4. A method for monitoring cattle risk according to any one of claims 1-3, wherein the step of inputting the feature information corresponding to the registration record information into a pre-trained gaussian distribution model to obtain the cattle identification result comprises the steps of:
inputting the characteristic information corresponding to the registration record information into the Gaussian distribution model to output a probability numerical value;
if the probability value reaches a first preset threshold value, determining that the user identity corresponding to the registration request is a non-cattle;
if the probability numerical value reaches a second preset threshold value and does not reach a first preset threshold value, determining that the user identity corresponding to the registration request is a suspected cattle;
and if the probability numerical value does not reach a second preset threshold value, determining that the user identity corresponding to the registration request is a cattle.
5. The method for monitoring the risk of cattle according to claim 4, wherein the step of inputting the feature information corresponding to the registration record information into a pre-trained Gaussian distribution model to obtain the cattle recognition result comprises:
if the user identity is not a cattle, receiving the registration request;
if the user identity is not a cattle, initiating identity authentication and selecting whether to accept the registration request or not according to an authentication result;
and if the user identity is the cattle, rejecting the registration request.
6. A terminal device, comprising:
the data acquisition module is used for acquiring a registration request and calling registration record information according to the registration request;
and the cattle recognition module is used for inputting the characteristic information corresponding to the registration record information into a pre-trained Gaussian distribution model to obtain a cattle recognition result.
7. The terminal device of claim 6, further comprising:
the sample acquisition module is used for acquiring a training set and a test set; the training set and the test set respectively contain cattle registration sample data and non-cattle registration sample data;
the characteristic extraction module is used for acquiring a characteristic set corresponding to each sample data in the training set;
and the model acquisition module is used for establishing a Gaussian distribution model according to the feature set, inputting feature information corresponding to the registration data in the test set into the Gaussian distribution model, calculating the identification accuracy of the Gaussian distribution model, determining whether to adjust the parameter variable of the Gaussian distribution model according to the accuracy, and determining the Gaussian distribution model with the accuracy reaching a preset value as the pre-trained Gaussian distribution model.
8. The terminal device according to claim 6 or 7, wherein the cattle identification module comprises:
the processing unit is used for inputting the characteristic information corresponding to the registration record information into the Gaussian distribution model and outputting a probability numerical value;
the identification unit is used for determining that the user identity corresponding to the registration request is a non-cattle if the probability value reaches a first preset threshold value; if the probability numerical value reaches a second preset threshold value and does not reach a first preset threshold value, determining that the user identity corresponding to the registration request is a suspected cattle; and if the probability numerical value does not reach a second preset threshold value, determining that the user identity corresponding to the registration request is a cattle.
9. The terminal device according to claim 8, further comprising:
the processing module is used for receiving the registration request if the user identity is not a cattle; if the user identity is not a cattle, initiating identity authentication and selecting whether to accept the registration request or not according to an authentication result; and if the user identity is the cattle, rejecting the registration request.
10. A storage medium having stored therein at least one instruction which is loaded and executed by a processor to perform the operations performed by the cattle risk monitoring method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010406854.5A CN111598162A (en) | 2020-05-14 | 2020-05-14 | Cattle risk monitoring method, terminal equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010406854.5A CN111598162A (en) | 2020-05-14 | 2020-05-14 | Cattle risk monitoring method, terminal equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111598162A true CN111598162A (en) | 2020-08-28 |
Family
ID=72183718
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010406854.5A Pending CN111598162A (en) | 2020-05-14 | 2020-05-14 | Cattle risk monitoring method, terminal equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111598162A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112307047A (en) * | 2020-10-29 | 2021-02-02 | 中国平安财产保险股份有限公司 | Production problem processing method, device, equipment and storage medium |
| CN112466449A (en) * | 2020-12-08 | 2021-03-09 | 微医云(杭州)控股有限公司 | Resource release method and device, electronic equipment and storage medium |
| CN112785021A (en) * | 2021-01-28 | 2021-05-11 | 联仁健康医疗大数据科技股份有限公司 | Reservation request response method and device, electronic equipment and storage medium |
| CN113205876A (en) * | 2021-07-06 | 2021-08-03 | 明品云(北京)数据科技有限公司 | Method, system, electronic device and medium for determining effective clues of target person |
| CN113947874A (en) * | 2021-09-01 | 2022-01-18 | 北京声智科技有限公司 | Data processing method and device, electronic equipment and readable storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105610649A (en) * | 2016-02-22 | 2016-05-25 | 太仓苏易信息科技有限公司 | Registration blacklist monitoring system |
| CN106453357A (en) * | 2016-11-01 | 2017-02-22 | 北京红马传媒文化发展有限公司 | Network ticket buying abnormal behavior recognition method and system and equipment |
| US20170155664A1 (en) * | 2014-05-12 | 2017-06-01 | Tencent Technology (Shenzhen) Company Limited | Method and apparatus for identifying malicious account |
| CN107147621A (en) * | 2017-04-20 | 2017-09-08 | 微医集团（浙江）有限公司 | The implementation method of internet medical treatment ox risk control |
| CN107527223A (en) * | 2016-12-22 | 2017-12-29 | 北京锐安科技有限公司 | A kind of method and device of Ticketing information analysis |
| CN109215794A (en) * | 2017-07-05 | 2019-01-15 | 东软集团股份有限公司 | The recognition methods of abnormal user and device, storage medium, electronic equipment |
| CN110263157A (en) * | 2019-05-24 | 2019-09-20 | 阿里巴巴集团控股有限公司 | A kind of data Risk Forecast Method, device and equipment |
| CN110675228A (en) * | 2019-09-27 | 2020-01-10 | 支付宝(杭州)信息技术有限公司 | User ticket buying behavior detection method and device |
-
2020
- 2020-05-14 CN CN202010406854.5A patent/CN111598162A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170155664A1 (en) * | 2014-05-12 | 2017-06-01 | Tencent Technology (Shenzhen) Company Limited | Method and apparatus for identifying malicious account |
| CN105610649A (en) * | 2016-02-22 | 2016-05-25 | 太仓苏易信息科技有限公司 | Registration blacklist monitoring system |
| CN106453357A (en) * | 2016-11-01 | 2017-02-22 | 北京红马传媒文化发展有限公司 | Network ticket buying abnormal behavior recognition method and system and equipment |
| CN107527223A (en) * | 2016-12-22 | 2017-12-29 | 北京锐安科技有限公司 | A kind of method and device of Ticketing information analysis |
| CN107147621A (en) * | 2017-04-20 | 2017-09-08 | 微医集团（浙江）有限公司 | The implementation method of internet medical treatment ox risk control |
| CN109215794A (en) * | 2017-07-05 | 2019-01-15 | 东软集团股份有限公司 | The recognition methods of abnormal user and device, storage medium, electronic equipment |
| CN110263157A (en) * | 2019-05-24 | 2019-09-20 | 阿里巴巴集团控股有限公司 | A kind of data Risk Forecast Method, device and equipment |
| CN110675228A (en) * | 2019-09-27 | 2020-01-10 | 支付宝(杭州)信息技术有限公司 | User ticket buying behavior detection method and device |
Non-Patent Citations (3)
| Title |
|---|
| A. S. ABDULL SUKOR 等: "Abnormality Detection Approach using Deep Learning Models in Smart Home Environments", 《ICCBN \'19: PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND BROADBAND NETWORKING》, pages 22 - 27 * |
| 刘志强: "基于顾客补偿推荐的电商黄牛治理研究", 《中国优秀硕士学位论文全文数据库 经济与管理科学辑》, no. 02, pages 157 - 136 * |
| 蒋鹏飞: "浏览器用户行为识别技术的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》, no. 04, pages 139 - 555 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112307047A (en) * | 2020-10-29 | 2021-02-02 | 中国平安财产保险股份有限公司 | Production problem processing method, device, equipment and storage medium |
| CN112307047B (en) * | 2020-10-29 | 2023-07-14 | 中国平安财产保险股份有限公司 | Production problem processing method, device, equipment and storage medium |
| CN112466449A (en) * | 2020-12-08 | 2021-03-09 | 微医云(杭州)控股有限公司 | Resource release method and device, electronic equipment and storage medium |
| CN112785021A (en) * | 2021-01-28 | 2021-05-11 | 联仁健康医疗大数据科技股份有限公司 | Reservation request response method and device, electronic equipment and storage medium |
| CN113205876A (en) * | 2021-07-06 | 2021-08-03 | 明品云(北京)数据科技有限公司 | Method, system, electronic device and medium for determining effective clues of target person |
| CN113947874A (en) * | 2021-09-01 | 2022-01-18 | 北京声智科技有限公司 | Data processing method and device, electronic equipment and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111598162A (en) | Cattle risk monitoring method, terminal equipment and storage medium | |
| CN107393541B (en) | Information verification method and device | |
| EP3306867B1 (en) | Auto-response method, apparatus and device, and computer-readable storage medium | |
| WO2020211247A1 (en) | Account information login method and device, computer device and computer storage medium | |
| CN107872772B (en) | Method and device for detecting fraud short messages | |
| CN110533085B (en) | Same-person identification method and device, storage medium and computer equipment | |
| CN111340558B (en) | Online information processing method, device, equipment and medium based on federal learning | |
| US20180293620A1 (en) | Information recommendation method, terminal, server, and computer storage medium | |
| CN112685799B (en) | Device fingerprint generation method and device, electronic device and computer readable medium | |
| CN110022399B (en) | Message display method and device, user terminal and readable storage medium | |
| CN112035717A (en) | Information processing method, device, equipment and storage medium | |
| CN108681871B (en) | Information prompting method, terminal equipment and computer readable storage medium | |
| CN108805332B (en) | Feature evaluation method and device | |
| WO2019205324A1 (en) | Task allocation method and system, and terminal device | |
| WO2022152018A1 (en) | Method and device for identifying multiple accounts belonging to the same person | |
| CN112150252A (en) | Credit-based service processing method and device | |
| CN101996230B (en) | Information processing apparatus, reference value determination method, and program | |
| CN113221005A (en) | Customer service pushing method, server and related products | |
| CN110309379B (en) | Product recommendation method, device, equipment and computer readable storage medium | |
| CN106056377A (en) | Data processing method and smartwatch | |
| CN112182520A (en) | Illegal account identification method and device, readable medium and electronic equipment | |
| CN113794678A (en) | Method and device compatible with multiple login modes and computer storage medium | |
| CN107368597B (en) | Information output method and device | |
| US20150227974A1 (en) | Membership Processing Method Performed According to Franchise Registration Request of Customer, and Device and System Therefor | |
| CN109165328A (en) | A kind of method for authenticating user identity and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |